Monitoring Environmentally Friendly Agriculture for Methane Emission Reduction: A High-Resolution Multi-Sensor Remote Sensing Protocol on Google Earth Engine

Reducing methane (CH₄​) emissions through environmentally friendly agriculture, such as Alternate Wetting and Drying (AWD), is a critical strategy for climate change mitigation in rice production. To effectively implement and evaluate these mitigation measures, it is essential to monitor agricultural practices and environmental variables at a high spatial resolution. This study develops a standardized data-processing protocol, which leverages Google Earth Engine (GEE) to generate high-resolution remote sensing features necessary for quantifying CH_4​ emissions.

The protocol integrates multi-sensor satellite data to capture the spatio-temporal dynamics of sustainable rice farming. Central to this protocol is the use of Sentinel-1 Synthetic Aperture Radar (SAR) data to classify water management regimes, specifically distinguishing between continuous flooding (CF) and AWD at the pixel level. Additionally, Sentinel-2 optical imagery is processed to extract key vegetation indices (e.g., NDVI, GRVI) to monitor crop growth. To address environmental factors, coarse-resolution soil moisture data from SMAP is downscaled to resolution by incorporating Sentinel-2 and Digital Elevation Model (DEM) data.

By synthesizing these multi-sensor inputs, the protocol provides the necessary foundation for mapping methane emission hotspots and assessing the impact of environmentally friendly management practices. This high-resolution approach supports the design of region-specific mitigation strategies and the advancement of climate-smart agriculture.

As for future research plans, we will apply the constructed model with the field-measured validation data to the extensive rice paddies in southern Ibaraki Prefecture in Japan to estimate methane emissions on a pixel-by-pixel basis and create hotspot maps. This enables the upscaling of a single-point observation model to a broader area while reflecting regional characteristics. This methodology is expected to serve as a powerful tool for examining highly effective methane reduction measures (such as utilization under the J-Credit system) based on each region's agricultural practices and environmental conditions.